An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example

NFFGRAM: Nonlinear Multi-Feature Fusion and Gated Recurrent Self-Attention Mechanism for Traditional Chinese Medicine Formula Recommendation

Traditional Chinese Medicine (TCM) prescriptions are derived from the distinctive thought process and clinical experiences of Chinese medical theory. With the advent of artificial intelligence (AI), there is an enhanced ability to formulate these prescriptions by analyzing symptom data. However, the inherent sparseness of herb-symptom association data still limits the efficacy of such predictive methods. This study introduces an enhanced bipartite graph diffusion algorithm coupled with a gated recurrent self-attention mechanism for predicting herb and symptom associations. The initial phase involves the reconstruction of the herb-symptom association matrix, leveraging the fractal-weighted K-nearest neighbor algorithm. Subsequently, a method is conceived to extract analogous features between herbs and symptoms, which integrates linear neighborhood similarity with Gaussian kernel similarity, both based on fractal dimensions. The next stage employs a modified bipartite graph diffusion to deduce underlying herb-symptom relationships. This process culminates with the integration of the gated recurrent self-attention mechanism and a confidence scoring system to refine the herb-symptom association predictive matrix at a granular level. We benchmark our results against leading-edge algorithms to ascertain the precision and reliability of our model. Such as improvements of precision@20 by 21.77%, recall@20 by 12.46%, and F1-score@20 by 19.28% compared with the best baseline for the TCM2 dataset. Additionally, comprehensive case studies are undertaken, evaluating recommended prescriptions using insights from contemporary medicine and network pharmacology. The proposed model provides a novel paradigm for enhancing herbal prescription methodologies and TCM herb-based treatments.

Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital

In recent years, artificial intelligence (AI) has shown promising applications in various scientific domains, including biochemical analysis research. However, the effectiveness of AI in modeling small-scale, imbalanced datasets remains an open question in such fields. This study explores the capabilities of eight basic AI algorithms, including ridge regression, logistic regression, random forest regression, and others, in modeling a small, imbalanced clinical dataset (total n = 387, class 0 = 27, class 1 = 360) related to the records of the biochemical blood tests from the patients with multiple wasp stings (MWS). Through rigorous evaluation using k-fold cross-validation and comprehensive scoring, we found that none of the models could effectively model the data. Even after fine-tuning the hyperparameters of the best-performing models, the results remained below acceptable thresholds. The study highlights the challenges of applying AI to small-scale datasets with imbalanced groups in biochemical or clinical research and emphasizes the need for novel algorithms tailored to small-scale data. The findings also call for further exploration into techniques such as transfer learning and data augmentation, and they underline the importance of understanding the minimum dataset scale required for effective AI modeling in biochemical contexts.

Exploring the action mechanism of Jinxin oral liquid on asthma by network pharmacology, molecular docking, and microRNA recognition

Using network pharmacology, molecular docking, and microRNA recognition, we have elucidated the mechanisms underlying the treatment of asthma by Jinxin oral liquid (JXOL). We began by identifying and normalizing the active compounds in JXOL through searches in the traditional Chinese medicine systems pharmacology database, SwissADME database, encyclopedia of traditional Chinese medicine database, HERB database, and PubChem. Subsequently, we gathered and standardized the targets of these active compounds from sources including the encyclopedia of traditional Chinese medicine database, similarity ensemble approach dataset, UniProt, and other databases. Disease targets were extracted from GeneCards, PharmGKB, OMIM, comparative toxicogenomics database, and DisGeNET. The intersection of targets between JXOL and asthma was determined using a Venn diagram. We visualized a Formula-Herb-Compound-Target-Disease network and a protein-protein interaction network using Cytoscape 3.9.0. Molecular docking studies were performed using Schrodinger software. To identify pathways related to asthma, we conducted gene ontology functional analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis using Metascape. MicroRNAs regulating the hub genes were obtained from the miRTarBase database, and a network linking these targets and miRNAs was constructed. Finally, we found 88 bioactive components in JXOL and 218 common targets with asthma. Molecular docking showed JXOL key compounds strongly bind to HUB targets. According to gene ontology biological process analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis, the PI3K-Akt signaling pathway, the MAPK signaling pathway, or the cAMP signaling pathway play a key role in treating of asthma by JXOL. The HUB target-miRNA network showed that 6 miRNAs were recognized. In our study, we have revealed for the first time the unique components, multiple targets, and diverse pathways in JXOL that underlie its mechanism of action in treating asthma through miRNAs.

Screening potential treatments for mpox from Traditional Chinese Medicine by using a data-driven approach

Mpox (MPX) has escalated into a public health emergency of international concern, necessitating urgent prophylactic and therapeutic measures. The primary goal of this investigation was to systematically extract Wan Quan's expertise in treating smallpox, as documented in Exclusive Methods for Treating Pox (Dou Zhen Xin Fa in Chinese), with the aim of identifying potential prescriptions, herbs, and components for alternative MPX therapies or drugs. This research utilized data mining to identify high-frequency Chinese Medicines (CMs), high-frequency CM-pairs, and CM compatibility rules. Network pharmacology, molecular docking, and molecular dynamic simulation were employed to reveal the potential molecular mechanisms of the core CM-pair. 119 prescriptions were extracted from Exclusive Methods for Treating Pox. We identified 25 high-frequency CMs and 23 high-frequency CM pairs among these prescriptions. Combined association rule mining analysis, Gancao (Glycyrrhiza uralensis Fisch.), Renshen (Panax ginseng C. A. Mey.), Danggui (Angelica sinensis (Oliv.) Diels), Shengma (Cimicifuga foetida L.), and Zicao (Lithospermum erythrorhizon Siebold & Zucc.) were selected as the core CM-pair for further investigation. Network pharmacology analysis yielded 131 active components and 348 candidate targets for the core CM-pair. Quercetin and celabenzine were chosen as ligands for molecular docking. GO and KEGG enrichment analyses revealed that the core CM-pair could interact with targets involved in immune, inflammatory, and infectious diseases. Moreover, key mpox virus targets, F8-A22-E4 DNA polymerase holoenzyme and profilin-like protein A42R, were docked well with the selected core components. And molecular dynamic simulation indicated that the component (quercetin) could stably bind to the target (profilin-like protein A42R). Our findings identified potential prescriptions, herbs, and components that can offer potential therapies or drugs for addressing the MPX epidemic.

Network pharmacology and molecular docking-based investigation on traditional Chinese medicine Astragalus membranaceus in oral ulcer treatment

To analyze the mechanism of Astragalus membranaceus (AM) in molecular level in the oral ulcer (OU) treatment with reference to network pharmacology. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database was used in screening the AM active components and AM action targets; GeneCards database was used to screen OU targets; the common target were screened by Venny online tool; Cytoscape software was applied to construct the target gene regulation map of AM active components; STRING database was used to construct the protein-protein interaction network and the key targets were screened as per degree value; gene ontology enrichment and KEGG pathway enrichment of interactive genes were calculated through David database. There were 17 active ingredients and 429 target spots in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database. There are 606 target genes for OU in GeneCards database. There are 67 common targets, including 10 key targets: IL10, IL6, TNF, IL1B, CXCL8, CCL2, TLR4, IL4, ICAM1, and IFNG. It involves 30 gene ontology terms and 20 KEGG signal channels. The molecular docking results showed that quercetin and kaempferol had a good binding activity with IL6, IL1B, TNF, and CCL2. Network pharmacological analysis shows that AM can regulate multiple signal pathways through multiple targets to treat OU.

Novel chemical-structure TPOR agonist, TMEA, promotes megakaryocytes differentiation and thrombopoiesis via mTOR and ERK signalings

BACKGROUND

Non-peptide thrombopoietin receptor (TPOR) agonists are promising therapies for the mitigation and treatment of thrombocytopenia. However, only few agents are available as safe and effective for stimulating platelet production for thrombocytopenic patients in the clinic.

PURPOSE

This study aimed to develop a novel small molecule TPOR agonist and investigate its underlying regulation of function in megakaryocytes (MKs) differentiation and thrombopoiesis.

METHODS

A potential active compound that promotes MKs differentiation and thrombopoiesis was obtained by machine learning (ML). Meanwhile, the effect was verified in zebrafish model, HEL and Meg-01 cells. Next, the key regulatory target was identified by Drug Affinity Responsive Target Stabilization Assay (DARTS), Cellular Thermal Shift Assay (CETSA), and molecular simulation experiments. After that, RNA-sequencing (RNA-seq) was used to further confirm the associated pathways and evaluate the gene expression induced during MK differentiation. In vivo, irradiation (IR) mice, C57BL/6N-TPORem1cyagen (Tpor-/-) mice were constructed by CRISPR/Cas9 technology to examine the therapeutic effect of TMEA on thrombocytopenia.

RESULTS

A natural chemical-structure small molecule TMEA was predicted to be a potential active compound based on ML. Obvious phenotypes of MKs differentiation were observed by TMEA induction in zebrafish model and TMEA could increase co-expression of CD41/CD42b, DNA content, and promote polyploidization and maturation of MKs in HEL and Meg-01 cells. Mechanically, TMEA could bind with TPOR protein and further regulate the PI3K/AKT/mTOR/P70S6K and MEK/ERK signal pathways. In vivo, TMEA evidently promoted platelet regeneration in mice with radiation-induced thrombocytopenia but had no effect on Tpor-/- and C57BL/6 (WT) mice.

CONCLUSION

TMEA could serve as a novel TPOR agonist to promote MKs differentiation and thrombopoiesis via mTOR and ERK signaling and could potentially be created as a promising new drug to treat thrombocytopenia.

Knowledge-Based Recurrent Neural Network for TCM Cerebral Palsy Diagnosis

Cerebral palsy is one of the most prevalent neurological disorders and the most frequent cause of disability. Identifying the syndrome by patients' symptoms is the key to traditional Chinese medicine (TCM) cerebral palsy treatment. Artificial intelligence (AI) is advancing quickly in several sectors, including TCM. AI will considerably enhance the dependability and precision of diagnoses, expanding effective treatment methods' usage. Thus, for cerebral palsy, it is necessary to build a decision-making model to aid in the syndrome diagnosis process. While the recurrent neural network (RNN) model has the potential to capture the correlation between symptoms and syndromes from electronic medical records (EMRs), it lacks TCM knowledge. To make the model benefit from both TCM knowledge and EMRs, unlike the ordinary training routine, we begin by constructing a knowledge-based RNN (KBRNN) based on the cerebral palsy knowledge graph for domain knowledge. More specifically, we design an evolution algorithm for extracting knowledge in the cerebral palsy knowledge graph. Then, we embed the knowledge into tensors and inject them into the RNN. In addition, the KBRNN can benefit from the labeled EMRs. We use EMRs to fine-tune the KBRNN, which improves prediction accuracy. Our study shows that knowledge injection can effectively improve the model effect. The KBRNN can achieve 79.31% diagnostic accuracy with only knowledge injection. Moreover, the KBRNN can be further trained by the EMRs. The results show that the accuracy of fully trained KBRNN is 83.12%.

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization's functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents' relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users' queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review

Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status. Objective: To categorize and seek the current usage of AI in CAM. Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies. Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians. Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.

Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning

ETHNOPHARMACOLOGICAL RELEVANCE

The novel coronavirus disease (COVID-19) outbreak in Wuhan has imposed a huge influence in terms of public health and economy on society. However, no effective drugs or vaccines have been developed so far. Traditional Chinese Medicine (TCM) has been considered as a promising supplementary treatment of this disease due to its clinically proven performance in many severe diseases, like severe acute respiratory syndrome (SARS). Meanwhile, many reports suggest that the side-effects (SE) of TCM prescriptions cannot be ignored in treating COVID-19 as it often leads to dramatic degradation of the patients' physical condition. Systematic evaluation of TCM regarding its latent SE becomes a burning issue.

AIM

In this study, we used an ontology-based side-effect prediction framework (OSPF) developed from our previous work and Artificial Neural Network (ANN)-based deep learning, to evaluate the TCM prescriptions officially recommended by China for the treatment of COVID-19.

MATERIALS AND METHODS

The OSPF developed from our previous work was implemented in this study, where an ontology-based model separated all ingredients in a TCM prescription into two categories: hot and cold. A database was created by converting each TCM prescription into a vector which contained ingredient dosages, corresponding hot/cold attribution and safe/unsafe labels. This allowed for training of the ANN model. A safety indicator (SI), as a complement to SE possibility, was then assigned to each TCM prescription. According to the proposed SI, from high to low, the recommended prescription list could be optimized. Furthermore, in interest of expanding the potential treatment options, SIs of other well-known TCM prescriptions, which are not included in the recommended list but are used traditionally to cure flu-like diseases, are also evaluated via this method.

RESULTS

Based on SI, QFPD-T, HSBD-F, PMSP, GCT-CJ, SF-ZSY, and HSYF-F were the safest treatments in the recommended list, with SI scores over 0.8. PESP, QYLX-F, JHQG-KL, SFJD-JN, SHL-KFY, PESP1, XBJ-ZSY, HSZF-F, PSSP2, FFTS-W, and NHSQ-W were the prescriptions most likely to be unsafe, with SI scores below 0.1. In the additional lists of other TCM prescriptions, the indicators of XC-T, SQRS-S, CC-J, and XFBD-F were all above 0.8, while QF-Y, XZXS-S, BJ-S, KBD-CJ, and QWJD-T's indicators were all below 0.1.

CONCLUSIONS

In total, there were 10 TCM prescriptions with indicators over 0.8, suggesting that they could be considered in treating COVID-19, if suitable. We believe this work could provide reasonable suggestions for choosing proper TCM prescriptions as a supplementary treatment for COVID-19. Furthermore, this work introduces a novel and informative method which could help create recommendation list of TCM prescriptions for the treatment of other diseases.

Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning

ETHNOPHARMACOLOGICAL RELEVANCE

The novel coronavirus disease (COVID-19) outbreak in Wuhan has imposed a huge influence in terms of public health and economy on society. However, no effective drugs or vaccines have been developed so far. Traditional Chinese Medicine (TCM) has been considered as a promising supplementary treatment of this disease due to its clinically proven performance in many severe diseases, like severe acute respiratory syndrome (SARS). Meanwhile, many reports suggest that the side-effects (SE) of TCM prescriptions cannot be ignored in treating COVID-19 as it often leads to dramatic degradation of the patients' physical condition. Systematic evaluation of TCM regarding its latent SE becomes a burning issue.

AIM

In this study, we used an ontology-based side-effect prediction framework (OSPF) developed from our previous work and Artificial Neural Network (ANN)-based deep learning, to evaluate the TCM prescriptions officially recommended by China for the treatment of COVID-19.

MATERIALS AND METHODS

The OSPF developed from our previous work was implemented in this study, where an ontology-based model separated all ingredients in a TCM prescription into two categories: hot and cold. A database was created by converting each TCM prescription into a vector which contained ingredient dosages, corresponding hot/cold attribution and safe/unsafe labels. This allowed for training of the ANN model. A safety indicator (SI), as a complement to SE possibility, was then assigned to each TCM prescription. According to the proposed SI, from high to low, the recommended prescription list could be optimized. Furthermore, in interest of expanding the potential treatment options, SIs of other well-known TCM prescriptions, which are not included in the recommended list but are used traditionally to cure flu-like diseases, are also evaluated via this method.

RESULTS

Based on SI, QFPD-T, HSBD-F, PMSP, GCT-CJ, SF-ZSY, and HSYF-F were the safest treatments in the recommended list, with SI scores over 0.8. PESP, QYLX-F, JHQG-KL, SFJD-JN, SHL-KFY, PESP1, XBJ-ZSY, HSZF-F, PSSP2, FFTS-W, and NHSQ-W were the prescriptions most likely to be unsafe, with SI scores below 0.1. In the additional lists of other TCM prescriptions, the indicators of XC-T, SQRS-S, CC-J, and XFBD-F were all above 0.8, while QF-Y, XZXS-S, BJ-S, KBD-CJ, and QWJD-T's indicators were all below 0.1.

CONCLUSIONS

In total, there were 10 TCM prescriptions with indicators over 0.8, suggesting that they could be considered in treating COVID-19, if suitable. We believe this work could provide reasonable suggestions for choosing proper TCM prescriptions as a supplementary treatment for COVID-19. Furthermore, this work introduces a novel and informative method which could help create recommendation list of TCM prescriptions for the treatment of other diseases.

Treasuring the computational approach in medicinal plant research

Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.

Development and Application of Artificial Intelligence in Auxiliary TCM Diagnosis

As an emerging comprehensive discipline, artificial intelligence (AI) has been widely applied in various fields, including traditional Chinese medicine (TCM), a treasure of the Chinese nation. Realizing the organic combination of AI and TCM can promote the inheritance and development of TCM. The paper summarizes the development and application of AI in auxiliary TCM diagnosis, analyzes the bottleneck of artificial intelligence in the field of auxiliary TCM diagnosis at present, and proposes a possible future direction of its development.

eHealth technologies assisting in identifying potential adverse interactions with complementary and alternative medicine (CAM) or standalone CAM adverse events or side effects: a scoping review

BACKGROUND

While there are several existing eHealth technologies for drug-drug interactions and stand-alone drug adverse effects, it appears that considerably less attention is focussed on that of complementary and alternative medicine (CAM). Despite poor knowledge of their potential interactions and side effects, many patients use CAM. This justifies the need to identify what eHealth technologies are assisting in identifying potential 1) adverse drug interactions with CAM, 2) adverse CAM-CAM interactions or 3) standalone CAM adverse events or side effects.

METHODS

A scoping review was conducted to identify eHealth technologies assisting in identifying potential adverse interactions with CAM or standalone CAM adverse events or side effects, following Arksey and O'Malley's five-stage scoping review framework. MEDLINE, EMBASE, and AMED databases and the Canadian Agency for Drugs and Technologies in Health website were systematically searched. Eligible articles had to have assessed or referenced an eHealth technology assisting in identifying potential one or more of the three aforementioned items. We placed no eligibility restrictions on type of eHealth technology.

RESULTS

Searches identified 3467 items, of which 2763 were unique, and 2674 titles and abstracts were eliminated, leaving 89 full-text articles to be considered. Of those, 48 were not eligible, leaving a total of 41 articles eligible for review. From these 41 articles, 69 unique eHealth technologies meeting our eligibility criteria were identified. Themes which emerged from our analysis included the following: the lack of recent reviews of CAM-related healthcare information; a large number of databases; and the presence of government adverse drug/event surveillance.

CONCLUSIONS

The present scoping review is the first, to our knowledge, to provide a descriptive map of the literature and eHealth technologies relating to our research question. We highlight that while an ample number of resources are available to healthcare providers, researchers, and patients, we caution that the quality and update frequency for many of these resources vary widely, and until formally assessed, remain unknown. We identify that a need exists to conduct an updated and systematically-searched review of CAM-related healthcare or research resources, as well as develop guidance documents associated with the development and evaluation of CAM-related eHealth technologies.